1. Remote Sensing of the Hydrological Cycle Phil Arkin, Cooperative Institute for Climate and Satellites Earth System Science Interdisciplinary Center, University of Maryland

2. Outline What is the hydrological cycle? What is the hydrological cycle? What sorts of interesting questions can we ask? What sorts of interesting questions can we ask? What observations are available? What observations are available? What can we do with those observations? What can we do with those observations? What do the results tell us? What do the results tell us? What do we need to do better? What do we need to do better?

3. What is the hydrological cycle? (depends on what you’re talking about, of course) For the Earth, it’s the reservoirs of water and the transfers among them For the Earth, it’s the reservoirs of water and the transfers among them It matters to the climate because of water’s ability to transfer heat in a latent state It matters to the climate because of water’s ability to transfer heat in a latent state It matters to people because precipitation is the original source of almost all fresh water we use It matters to people because precipitation is the original source of almost all fresh water we use (From UCAR web site)

4. Vertically integrated water balance equation for the atmosphere - liquid and solid water small compared to vapor – neglected here - balance is between changes in storage (vertically integrated specific humidity or precipitable water) and horizontal convergence, evaporation and precipitation

5. What does this mean? And what can we do with it? Increase in water in atmosphere = Horizontal amount coming in – Horizontal amount leaving + Evaporation from surface – Precipitation to surface Divide atmosphere into boxes Divide atmosphere into boxes Calculate each quantity in each box Calculate each quantity in each box Do that every few hours Do that every few hours That should be enough to describe the hydrological cycle (water budget) That should be enough to describe the hydrological cycle (water budget)

6. Outline What is the hydrological cycle? What is the hydrological cycle? What sorts of interesting questions can we ask? What sorts of interesting questions can we ask? What observations are available? What observations are available? What can we do with those observations? What can we do with those observations? What do the results tell us? What do the results tell us? What do we need to do better? What do we need to do better?

7. Research Results Climate models indicate that global warming (or cooling) will be accompanied by changes in water vapor and precipitation: Climate models indicate that global warming (or cooling) will be accompanied by changes in water vapor and precipitation: Water vapor changes to maintain roughly constant relative humidity (about 7% per degree) Water vapor changes to maintain roughly constant relative humidity (about 7% per degree) Precipitation changes in the same direction as water vapor but at a slower rate (about 2-3% per degree) Precipitation changes in the same direction as water vapor but at a slower rate (about 2-3% per degree) That’s for global averages – regional changes will vary That’s for global averages – regional changes will vary Observations show: Observations show: Global water vapor has increased recently as temperatures have warmed Global water vapor has increased recently as temperatures have warmed Global precipitation has increased also, but there is less agreement on the amount of change Global precipitation has increased also, but there is less agreement on the amount of change Just how good are the observations? Just how good are the observations? Can they tell us how good the models are? Can they tell us how good the models are?

8. Scientific Questions Does water vapor in the atmosphere track surface temperature in the manner than the models predict? Does water vapor in the atmosphere track surface temperature in the manner than the models predict? How well do we know how much precipitation falls, both globally and regionally, during the satellite era? How well do we know how much precipitation falls, both globally and regionally, during the satellite era? Why do I say “satellite era”? Why do I say “satellite era”? Where can available datasets be improved? Where can available datasets be improved? Can we say anything about global precipitation variability prior to satellite observations? Can we say anything about global precipitation variability prior to satellite observations? Can these observations help us check climate model simulations (such as those used in the Intergovernmental Panel on Climate Change assessments)? Can these observations help us check climate model simulations (such as those used in the Intergovernmental Panel on Climate Change assessments)?

9. 21 st Century Changes in Regional Precipitation IPCC AR4 Summary for Policy Makers (Figure SPM.7) Are projections like these realistic enough to merit action by society?

10. Outline What is the hydrological cycle? What is the hydrological cycle? What sorts of interesting questions can we ask? What sorts of interesting questions can we ask? What observations are available? What observations are available? What can we do with those observations? What can we do with those observations? What do the results tell us? What do the results tell us? What do we need to do better? What do we need to do better?

11. Evaporation No actual observations of evaporation exist – not really an observable quantity No actual observations of evaporation exist – not really an observable quantity Relatively simple models based on parameterizations of turbulent fluxes can be used to calculate oceanic evaporation Relatively simple models based on parameterizations of turbulent fluxes can be used to calculate oceanic evaporation Require observations of wind speed, near-surface gradient in temperature/humidity Require observations of wind speed, near-surface gradient in temperature/humidity Satellite-derived estimates of SST and wind speed are available and can be used Satellite-derived estimates of SST and wind speed are available and can be used Over land, what’s needed is evapotranspiration (except in deserts) Over land, what’s needed is evapotranspiration (except in deserts) In addition to wind speed, temperature and humidity, requires surface roughness and vegetation activity In addition to wind speed, temperature and humidity, requires surface roughness and vegetation activity No good way to measure (quantitatively) some of these No good way to measure (quantitatively) some of these Global evaporation/evapotranspiration datasets exist, but are based on global weather/climate models – confidence in their details is low Global evaporation/evapotranspiration datasets exist, but are based on global weather/climate models – confidence in their details is low

12. Atmospheric Water Vapor Radiosonde observations include relative humidity; combined with temperature can be used to calculate specific humidity/water vapor Radiosonde observations include relative humidity; combined with temperature can be used to calculate specific humidity/water vapor Poor sampling Poor sampling Significant instrumental errors Significant instrumental errors Satellite observations can be used to estimate total column water vapor and its vertical profile – this has been done to a limited degree (one dataset exists) Satellite observations can be used to estimate total column water vapor and its vertical profile – this has been done to a limited degree (one dataset exists) NVAP (Randel and Vonder Haar, CSU) NVAP (Randel and Vonder Haar, CSU) 1988 – 1999 only 1988 – 1999 only Models can provide fields of water vapor based on the combination of observations and forecasts through data assimilation Models can provide fields of water vapor based on the combination of observations and forecasts through data assimilation Forecast models tend to deal with uncertainties by adjusting the water vapor, since the model adjusts it rapidly (thereby making the water vapor initial fields less useful) Forecast models tend to deal with uncertainties by adjusting the water vapor, since the model adjusts it rapidly (thereby making the water vapor initial fields less useful)

13. Precipitation The only direct, quantitative measurements come from rain gauges The only direct, quantitative measurements come from rain gauges Put a container near the surface (careful not to let trees or buildings get in the way!) and catch whatever rain falls Put a container near the surface (careful not to let trees or buildings get in the way!) and catch whatever rain falls Pretty good absolute accuracy (but not perfect) Pretty good absolute accuracy (but not perfect) Very limited spatial coverage (only where people are, and tough to get data sometimes) Very limited spatial coverage (only where people are, and tough to get data sometimes) Both measurement and sampling errors Both measurement and sampling errors Wind and solid precipitation Wind and solid precipitation In mountains, gauges tend to be in unrepresentative locations In mountains, gauges tend to be in unrepresentative locations Tough data processing problem – wide variety of formats and media Tough data processing problem – wide variety of formats and media

14. An Example for January 1994 Gauge-based analysis based on about 6500 gauges by Global Precipitation Climatology Centre, DWD

15. January 1994 Visible and/or infrared (IR) Visible and/or infrared (IR) Geostationary coverage nearly global (up to 60° latitude) Geostationary coverage nearly global (up to 60° latitude) 30 minute temporal sampling 30 minute temporal sampling Highly empirical - you really don’t see anything except the tops of the clouds Highly empirical - you really don’t see anything except the tops of the clouds Many years (20 - 30) available Many years (20 - 30) available Many, many examples - interestingly enough, almost any method seems to work to some extent Many, many examples - interestingly enough, almost any method seems to work to some extent

16. ScatteringEmission At lower frequencies, ocean surface is cold and raindrops appear warmer At lower frequencies, ocean surface is cold and raindrops appear warmer Ocean only at present Ocean only at present Best way to estimate “warm” rain (not associated with an ice phase) Best way to estimate “warm” rain (not associated with an ice phase) Also subject to errors from cold surface water or ice Also subject to errors from cold surface water or ice Most direct (physically based) of passive algorithms, but requires assumptions regarding atmosphere (freezing level) and surface emissivity Most direct (physically based) of passive algorithms, but requires assumptions regarding atmosphere (freezing level) and surface emissivity Above 50GHz, large ice particles scatter radiation upwelling from the surface – make storms look colder than background Above 50GHz, large ice particles scatter radiation upwelling from the surface – make storms look colder than background Works over land as well as ocean Works over land as well as ocean Good at detecting convective precipitation Good at detecting convective precipitation Not very useful over cold surface, especially ice or snow Not very useful over cold surface, especially ice or snow Algorithms more empirical than emission, less so than IR/visible – depend on statistical relationship between cloud ice and rain at surface Algorithms more empirical than emission, less so than IR/visible – depend on statistical relationship between cloud ice and rain at surface At microwave frequencies (10-100GHz), clouds are nearly transparent

17. Other satellite-derived estimates better in principle, but more difficult in practice Inversion - with adequate spectral resolution and a good radiative transfer model, vertical structure of rain/snow can be inferred Inversion - with adequate spectral resolution and a good radiative transfer model, vertical structure of rain/snow can be inferred SSM/I since 1987, AMSU, AMSR-E, TMI SSM/I since 1987, AMSU, AMSR-E, TMI Goddard Profiling Algorithm – GPROF, Kummerow Goddard Profiling Algorithm – GPROF, Kummerow Radar - in principle, best by far; in practice, only recently possible Radar - in principle, best by far; in practice, only recently possible TRMM, GPM TRMM, GPM

18. Model-derived estimates of precipitation Other atmospheric observations contain relevant information Other atmospheric observations contain relevant information Winds, temperature, moisture Winds, temperature, moisture Physically based dynamical models yield precipitation in various ways Physically based dynamical models yield precipitation in various ways NWP models forecast precipitation NWP models forecast precipitation Assimilation of radiances can yield cloud, hydrometeor distributions Assimilation of radiances can yield cloud, hydrometeor distributions Best where models best - mid, maybe high latitudes Best where models best - mid, maybe high latitudes

19. TMPA 3-HrlyCMORPH 3-Hrly MERRA 3-Hrly First 7 days of January 2004

20. Outline What is the hydrological cycle? What is the hydrological cycle? What sorts of interesting questions can we ask? What sorts of interesting questions can we ask? What observations are available? What observations are available? What can we do with those observations? What can we do with those observations? What do the results tell us? What do the results tell us? What do we need to do better? What do we need to do better?

21. Relatively little until we combine the data to make maps and time series Through “analysis” – any process for combining different observations to create a field or time series with no gaps Through “analysis” – any process for combining different observations to create a field or time series with no gaps Satellite-derived estimates have complementary characteristics, so combination makes sense Satellite-derived estimates have complementary characteristics, so combination makes sense Geostationary infrared is more complete but has poor accuracy, low Earth orbit passive microwave is more accurate but has sparse sampling Geostationary infrared is more complete but has poor accuracy, low Earth orbit passive microwave is more accurate but has sparse sampling Satellite-derived estimates have biases that can be reduced/removed by adding information from rain gauges Satellite-derived estimates have biases that can be reduced/removed by adding information from rain gauges Since the input data are not uniformly distributed in time and space, an analysis (method for creating complete in time and space fields from varying and incomplete observations) must be used to create the final dataset Since the input data are not uniformly distributed in time and space, an analysis (method for creating complete in time and space fields from varying and incomplete observations) must be used to create the final dataset Analysis process can be statistical combination of inputs, or simply a composite, or include an atmospheric model (often referred to as data assimilation) Analysis process can be statistical combination of inputs, or simply a composite, or include an atmospheric model (often referred to as data assimilation)

22. Global Precipitation Datasets GPCP (left)/CMAP (right) mean annual cycle and global mean time series Monthly/5-day; 2.5° lat/long global; both based on microwave/IR combined with gauges Both have greater (but poorly known) errors in high latitudes

23. Multi-Source Analysis of Precipitation (MSAP) Combines model precipitation with microwave-based estimates Combines model precipitation with microwave-based estimates Relies on satellite estimates in tropics, reanalysis in high latitudes, mix in between Relies on satellite estimates in tropics, reanalysis in high latitudes, mix in between

24. Outline What is the hydrological cycle? What is the hydrological cycle? What sorts of interesting questions can we ask? What sorts of interesting questions can we ask? What observations are available? What observations are available? What can we do with those observations? What can we do with those observations? What can the results tell us? What can the results tell us? What do we need to do better? What do we need to do better?

25. Global Averages - do models give the same global means as observations? Global Averages - do models give the same global means as observations? Trends - models project large increases in global mean temperature, accompanied with increases in water vapor and precipitation. Trends - models project large increases in global mean temperature, accompanied with increases in water vapor and precipitation. Do global datasets support these model results? Do global datasets support these model results? Annual Cycle - mean annual cycle of global temperature is substantial (much larger than 100 year trends) Annual Cycle - mean annual cycle of global temperature is substantial (much larger than 100 year trends) Is it associated with changes in water vapor and precipitation? Is it associated with changes in water vapor and precipitation?

26. Datasets based on observations (GPCP, CMAP) give about 2.6 mm/day (AR4 range is about 2.5-3.2 mm/day) Datasets based on observations (GPCP, CMAP) give about 2.6 mm/day (AR4 range is about 2.5-3.2 mm/day) Data assimilation products average about 3 mm/day; also have larger mean annual cycle and greater interannual variability Data assimilation products average about 3 mm/day; also have larger mean annual cycle and greater interannual variability MSAP might eventually provide improved analyses, but current DA systems appear to be a long way from providing believable global precipitation products

27. Climate Model-Based Precipitation Many of the models used in AR4 were also used to simulate the 20 th Century – precipitation from those runs can be compared to global precipitation datasets Many of the models used in AR4 were also used to simulate the 20 th Century – precipitation from those runs can be compared to global precipitation datasets These are anomalies – the models average about 0.2 mm/day globally greater than the observations These are anomalies – the models average about 0.2 mm/day globally greater than the observations

28. Mean annual cycle: T, P, E, WV from data assimilation

29. Mean annual cycle: Temperature and Precipitation from Observations Difference between CMAP and GPCP due to differences over the ocean – no independent validation available

30. Trends in global precipitation Since we don’t have satellite observations before about 1980, we have to use the observations we do have to make estimates Since we don’t have satellite observations before about 1980, we have to use the observations we do have to make estimates We use modern datasets combined with historical observations from rain gauges as well as sea level pressure and sea surface temperature datasets We use modern datasets combined with historical observations from rain gauges as well as sea level pressure and sea surface temperature datasets Shows an upward trend in oceanic precipitation Shows an upward trend in oceanic precipitation

31. Reconstruction Trends Tropical oceanic precipitation increases a lot Tropical oceanic precipitation increases a lot Land precipitation, especially in Northern Hemisphere, decreases a bit Land precipitation, especially in Northern Hemisphere, decreases a bit

32. Comparison against model simulations of the 20 th Century These are smoothed annual averages These are smoothed annual averages We computed joint empirical orthogonal functions, which can find the strongest common features between the models and observations We computed joint empirical orthogonal functions, which can find the strongest common features between the models and observations The trend is clear, with some similarity between the observations and the models The trend is clear, with some similarity between the observations and the models

33. Outline What is the hydrological cycle? What is the hydrological cycle? What sorts of interesting questions can we ask? What sorts of interesting questions can we ask? What observations are available? What observations are available? What can we do with those observations? What can we do with those observations? What do the results tell us? What do the results tell us? What do we need to do better? What do we need to do better?

34. Conclusions/Issues Global data sets needed to describe the global hydrological cycle require a combination of theoretical (model) and observation input Global data sets needed to describe the global hydrological cycle require a combination of theoretical (model) and observation input Water vapor probably best except for trends Water vapor probably best except for trends Precipitation usable, but lots of ways we could improve Precipitation usable, but lots of ways we could improve Evaporation dependent on model accuracy Evaporation dependent on model accuracy Water vapor short-term variations look good; not as good on longer time scales Water vapor short-term variations look good; not as good on longer time scales Precipitation variability: Precipitation variability: Trends – plausible, and consistent with models Trends – plausible, and consistent with models Global means – observed datasets agree with each other, but are lower than models Global means – observed datasets agree with each other, but are lower than models Aspects of interannual variation (El Niño) are realistic Aspects of interannual variation (El Niño) are realistic Useful for climate diagnostic studies and model verification Useful for climate diagnostic studies and model verification